/usr/lib/python2.7/dist-packages/dipy/core/tests/test_gradients.py is in python-dipy 0.10.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | from nose.tools import assert_true
import numpy as np
import numpy.testing as npt
from dipy.data import get_data
from dipy.core.gradients import (gradient_table, GradientTable,
gradient_table_from_bvals_bvecs)
from dipy.io.gradients import read_bvals_bvecs
def test_btable_prepare():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(7)
bvals[0] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt.info
fimg, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
bvecs = np.where(np.isnan(bvecs), 0, bvecs)
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(bt.bvecs, bvecs)
bt2 = gradient_table(bvals, bvecs.T)
npt.assert_array_equal(bt2.bvecs, bvecs)
btab = np.concatenate((bvals[:, None], bvecs), axis=1)
bt3 = gradient_table(btab)
npt.assert_array_equal(bt3.bvecs, bvecs)
npt.assert_array_equal(bt3.bvals, bvals)
bt4 = gradient_table(btab.T)
npt.assert_array_equal(bt4.bvecs, bvecs)
npt.assert_array_equal(bt4.bvals, bvals)
def test_GradientTable():
gradients = np.array([[0, 0, 0],
[1, 0, 0],
[0, 0, 1],
[3, 4, 0],
[5, 0, 12]], 'float')
expected_bvals = np.array([0, 1, 1, 5, 13])
expected_b0s_mask = expected_bvals == 0
expected_bvecs = gradients / (expected_bvals + expected_b0s_mask)[:, None]
gt = GradientTable(gradients, b0_threshold=0)
npt.assert_array_almost_equal(gt.bvals, expected_bvals)
npt.assert_array_equal(gt.b0s_mask, expected_b0s_mask)
npt.assert_array_almost_equal(gt.bvecs, expected_bvecs)
npt.assert_array_almost_equal(gt.gradients, gradients)
gt = GradientTable(gradients, b0_threshold=1)
npt.assert_array_equal(gt.b0s_mask, [1, 1, 1, 0, 0])
npt.assert_array_equal(gt.bvals, expected_bvals)
npt.assert_array_equal(gt.bvecs, expected_bvecs)
npt.assert_raises(ValueError, GradientTable, np.ones((6, 2)))
npt.assert_raises(ValueError, GradientTable, np.ones((6,)))
def test_gradient_table_from_bvals_bvecs():
sq2 = np.sqrt(2) / 2
bvals = [0, 1, 2, 3, 4, 5, 6, 0]
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2],
[0, 0, 0]])
gt = gradient_table_from_bvals_bvecs(bvals, bvecs, b0_threshold=0)
npt.assert_array_equal(gt.bvecs, bvecs)
npt.assert_array_equal(gt.bvals, bvals)
npt.assert_array_equal(gt.gradients, np.reshape(bvals, (-1, 1)) * bvecs)
npt.assert_array_equal(gt.b0s_mask, [1, 0, 0, 0, 0, 0, 0, 1])
# Test nans are replaced by 0
new_bvecs = bvecs.copy()
new_bvecs[[0, -1]] = np.nan
gt = gradient_table_from_bvals_bvecs(bvals, new_bvecs, b0_threshold=0)
npt.assert_array_equal(gt.bvecs, bvecs)
# Bvalue > 0 for non-unit vector
bad_bvals = [2, 1, 2, 3, 4, 5, 6, 0]
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bad_bvals,
bvecs, b0_threshold=0.)
# num_gard inconsistent bvals, bvecs
bad_bvals = np.ones(7)
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bad_bvals,
bvecs, b0_threshold=0.)
# bvals not 1d
bad_bvals = np.ones((1, 8))
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bad_bvals,
bvecs, b0_threshold=0.)
# bvec not 2d
bad_bvecs = np.ones((1, 8, 3))
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bvals,
bad_bvecs, b0_threshold=0.)
# bvec not (N, 3)
bad_bvecs = np.ones((8, 2))
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bvals,
bad_bvecs, b0_threshold=0.)
# bvecs not unit vectors
bad_bvecs = bvecs * 2
npt.assert_raises(ValueError, gradient_table_from_bvals_bvecs, bvals,
bad_bvecs, b0_threshold=0.)
# Test **kargs get passed along
gt = gradient_table_from_bvals_bvecs(bvals, bvecs, b0_threshold=0,
big_delta=5, small_delta=2)
npt.assert_equal(gt.big_delta, 5)
npt.assert_equal(gt.small_delta, 2)
def test_b0s():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(8)
bvals[0] = 0
bvals[7] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2],
[0, 0, 0]])
bt = gradient_table(bvals, bvecs)
npt.assert_array_equal(np.where(bt.b0s_mask > 0)[0], np.array([0, 7]))
npt.assert_array_equal(np.where(bt.b0s_mask == 0)[0], np.arange(1, 7))
def test_gtable_from_files():
fimg, fbvals, fbvecs = get_data('small_101D')
gt = gradient_table(fbvals, fbvecs)
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
npt.assert_array_equal(gt.bvals, bvals)
npt.assert_array_equal(gt.bvecs, bvecs)
def test_deltas():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(7)
bvals[0] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
bt = gradient_table(bvals, bvecs, big_delta=5, small_delta=2)
npt.assert_equal(bt.big_delta, 5)
npt.assert_equal(bt.small_delta, 2)
def test_qvalues():
sq2 = np.sqrt(2) / 2.
bvals = 1500 * np.ones(7)
bvals[0] = 0
bvecs = np.array([[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[sq2, sq2, 0],
[sq2, 0, sq2],
[0, sq2, sq2]])
qvals = np.sqrt(bvals / 6) / (2 * np.pi)
bt = gradient_table(bvals, bvecs, big_delta=8, small_delta=6)
npt.assert_almost_equal(bt.qvals, qvals)
if __name__ == "__main__":
from numpy.testing import run_module_suite
run_module_suite()
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